A survey of computer-based vision systems for automatic identification of  plant species

Babatunde, Oluleye Hezekiah; Armstrong, Leisa; Leng, Jinsong; Diepeveen, Dean

doi:10.17700/jai.2015.6.1.152

Cited by 28 publications

(18 citation statements)

References 17 publications

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“…Table I summarises some of the works that have been done in the automated recognition of medicinal and non-medicinal plant species during the last decade. Babatunde et al (2015) has done a good survey on different computer vision techniques and machine learning classifiers that have been used in this field during the last ten years [19]. Furthermore, Mata-Montero and Carranza-Rojas (2016) has provided a good introduction to the field and also discussed the challenges and opportunities in this domain [20].…”

Section: IImentioning

confidence: 99%

Automatic Recognition of Medicinal Plants using Machine Learning Techniques

Begue¹,

Kowlessur²,

Singh³

et al. 2017

ijacsa

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Abstract-The proper identification of plant species has major benefits for a wide range of stakeholders ranging from forestry services, botanists, taxonomists, physicians, pharmaceutical laboratories, organisations fighting for endangered species, government and the public at large. Consequently, this has fueled an interest in developing automated systems for the recognition of different plant species. A fully automated method for the recognition of medicinal plants using computer vision and machine learning techniques has been presented. Leaves from 24 different medicinal plant species were collected and photographed using a smartphone in a laboratory setting. A large number of features were extracted from each leaf such as its length, width, perimeter, area, number of vertices, colour, perimeter and area of hull. Several derived features were then computed from these attributes. The best results were obtained from a random forest classifier using a 10-fold crossvalidation technique. With an accuracy of 90.1%, the random forest classifier performed better than other machine learning approaches such as the k-nearest neighbour, naïve Bayes, support vector machines and neural networks. These results are very encouraging and future work will be geared towards using a larger dataset and high-performance computing facilities to investigate the performance of deep learning neural networks to identify medicinal plants used in primary health care. To the best of our knowledge, this work is the first of its kind to have created a unique image dataset for medicinal plants that are available on the island of Mauritius. It is anticipated that a web-based or mobile computer system for the automatic recognition of medicinal plants will help the local population to improve their knowledge on medicinal plants, help taxonomists to develop more efficient species identification techniques and will also contribute significantly in the protection of endangered species.

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Section: IImentioning

confidence: 99%

Automatic Recognition of Medicinal Plants using Machine Learning Techniques

Begue¹,

Kowlessur²,

Singh³

et al. 2017

ijacsa

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“…A plant species identification system for the broad leaves found in Norway was proposed by Babatunde et al [4] which were based on the morphological features of the leaves and they also discussed different features of leaves and feature extraction techniques. In [5], various leaf structures and flower feature extraction techniques and problems in an agricultural environment were reviewed.…”

Section: An Overview Of Species Identification Systemsmentioning

confidence: 99%

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Thyagharajan¹,

Raji²

2018

Arch Computat Methods Eng

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Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves. Table 2 Inferences of data sources Visual descriptors of leaf and classification No. of papers selected Geometrical descriptors 30 Leaf shape/tip/base/venation 40 Texture/texton 25

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“…A report on their weaknesses are shown in the last column. A deeper literature reviews on recent works on computer-based vision systems for plant species identification can be found in the paper (Babatunde, Armstrong, Leng & Diepeveen, 2015). Table 1.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…According a survey (Babatunde, Armstrong, Leng and Diepeveen, 2015) attempts to automate this process have been made, using features of plants extracted from images as input parameters to various classifier systems. This work further examines the application of genetically selected principal components combined with k Nearest Neighbor (kNN) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Babatunde

Armstrong

Leng

et al. 2015

JAI

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A B S T R A C TPrecision farming involves integration of different areas of disciplines to lower production costs and improve productivity. One major arm of precision farming or agriculture is the development of computer-based vision systems for automatic identification of plant species. This work involves application of k Nearest Neighbour (kNN) and genetic principal component analysis (GA-PCA) for the development of computer-based vision systems for automatic identification of plant species. As the first contribution, several image descriptors were extracted from the images of plants found in the Flavia dataset. Lots of these image features are affine maps and amalgamation of such massive features in one study is a novel idea. These descriptors are Zernike Moments (ZM), Fourier Descriptors (FDs), Lengendre Moments (LM) Hu 7 Moments, Texture, Geometrical properties and colour features. The GA-PCA (1907 x 41) feature space improved the classification accuracy of kNN from 84.98% to 88.75%.

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A survey of computer-based vision systems for automatic identification of plant species

Cited by 28 publications

References 17 publications

Automatic Recognition of Medicinal Plants using Machine Learning Techniques

Automatic Recognition of Medicinal Plants using Machine Learning Techniques

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

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